from sklearn_benchmarks.report import Reporting, ReportingHpo, print_time_report, print_env_info
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
%matplotlib notebook

print_time_report()

Time report

daal4py_KMeans_short: 0h 0m 1s

daal4py_Ridge: 0h 0m 1s

KMeans_short: 0h 0m 2s

daal4py_LogisticRegression: 0h 0m 3s

daal4py_KMeans_tall: 0h 0m 7s

Ridge: 0h 0m 10s

LogisticRegression: 0h 0m 19s

KMeans_tall: 0h 0m 20s

daal4py_KNeighborsClassifier_kd_tree: 0h 0m 26s

KNeighborsClassifier_kd_tree: 0h 2m 25s

daal4py_KNeighborsClassifier: 0h 2m 27s

xgboost: 0h 5m 5s

lightgbm: 0h 5m 9s

catboost_symmetric: 0h 5m 9s

HistGradientBoostingClassifier: 0h 5m 10s

catboost_lossguide: 0h 5m 34s

KNeighborsClassifier: 0h 38m 11s

total: 1h 10m 47s

print_env_info()

Benchmark environment

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.8.0-1033-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.21.0",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.5",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}

reporting = Reporting(config="config.yml")
reporting.run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.125	0.000	6.387	0.000	-1	100	NaN	NaN	0.433	0.000	0.289	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	30.787	0.000	0.000	0.031	-1	100	0.928	0.688	1.733	0.018	17.761	0.181	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.203	0.026	0.000	0.203	-1	100	1.000	0.000	0.080	0.000	2.540	0.327	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.123	0.000	6.517	0.000	-1	5	NaN	NaN	0.424	0.000	0.289	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	30.321	0.000	0.000	0.030	-1	5	0.811	0.688	1.727	0.006	17.554	0.065	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.185	0.015	0.000	0.185	-1	5	1.000	0.000	0.079	0.000	2.327	0.194	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.114	0.000	7.035	0.000	-1	1	NaN	NaN	0.422	0.000	0.269	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.418	0.135	0.000	0.022	-1	1	0.736	0.805	1.735	0.014	12.920	0.129	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.163	0.013	0.000	0.163	-1	1	0.000	1.000	0.079	0.001	2.059	0.162	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.119	0.000	6.722	0.000	1	5	NaN	NaN	0.424	0.000	0.281	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	20.861	0.033	0.000	0.021	1	5	0.811	0.944	1.792	0.014	11.643	0.090	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.195	0.001	0.000	0.195	1	5	1.000	1.000	0.079	0.000	2.464	0.018	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.115	0.000	6.954	0.000	1	1	NaN	NaN	0.426	0.000	0.270	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	11.784	0.015	0.000	0.012	1	1	0.736	0.805	1.734	0.016	6.798	0.065	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.181	0.001	0.000	0.181	1	1	0.000	1.000	0.079	0.000	2.279	0.021	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.115	0.000	6.973	0.000	1	100	NaN	NaN	0.425	0.000	0.270	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	20.907	0.037	0.000	0.021	1	100	0.928	0.944	1.800	0.024	11.612	0.159	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.194	0.001	0.000	0.194	1	100	1.000	1.000	0.082	0.006	2.370	0.162	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.302	0.000	-1	100	NaN	NaN	0.088	0.000	0.604	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	27.469	0.224	0.000	0.027	-1	100	0.979	0.973	0.270	0.006	101.872	2.242	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.026	0.002	0.000	0.026	-1	100	1.000	1.000	0.006	0.000	4.732	0.418	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.050	0.000	0.321	0.000	-1	5	NaN	NaN	0.088	0.000	0.566	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	27.633	0.396	0.000	0.028	-1	5	0.981	0.973	0.270	0.004	102.293	1.997	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.003	0.000	0.028	-1	5	1.000	1.000	0.005	0.000	5.277	0.559	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.297	0.000	-1	1	NaN	NaN	0.088	0.000	0.615	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.047	0.158	0.000	0.020	-1	1	0.974	0.979	0.270	0.004	74.256	1.246	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.020	0.002	0.000	0.020	-1	1	0.000	1.000	0.005	0.000	4.000	0.535	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.048	0.000	0.335	0.000	1	5	NaN	NaN	0.087	0.000	0.548	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	16.875	0.008	0.000	0.017	1	5	0.981	0.982	0.317	0.004	53.257	0.688	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.017	0.000	0.000	0.017	1	5	1.000	1.000	0.005	0.000	3.287	0.193	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.047	0.000	0.338	0.000	1	1	NaN	NaN	0.087	0.000	0.543	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	8.959	0.037	0.000	0.009	1	1	0.974	0.979	0.272	0.002	32.946	0.298	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.013	0.000	0.000	0.013	1	1	0.000	1.000	0.005	0.000	2.492	0.131	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.048	0.000	0.335	0.000	1	100	NaN	NaN	0.087	0.000	0.546	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	16.832	0.026	0.000	0.017	1	100	0.979	0.982	0.326	0.005	51.587	0.729	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.019	0.000	0.000	0.019	1	100	1.000	1.000	0.005	0.000	3.482	0.213	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.767	0.000	0.029	0.000	-1	5	NaN	NaN	0.643	0.000	4.306	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.755	0.010	0.000	0.001	-1	5	0.981	0.972	0.529	0.016	1.427	0.047	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	5	1.000	1.000	0.001	0.000	4.967	1.809	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.802	0.000	0.029	0.000	-1	100	NaN	NaN	0.651	0.000	4.304	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.604	0.033	0.000	0.003	-1	100	0.977	0.974	0.174	0.002	14.990	0.253	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.007	0.000	0.000	0.007	-1	100	1.000	1.000	0.000	0.000	26.432	10.513	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.008	0.000	0.027	0.000	-1	1	NaN	NaN	0.661	0.000	4.553	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.432	0.009	0.000	0.000	-1	1	0.964	0.974	0.173	0.002	2.497	0.056	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	9.644	4.578	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.755	0.000	0.029	0.000	1	1	NaN	NaN	0.660	0.000	4.175	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.696	0.004	0.000	0.001	1	1	0.964	0.958	0.094	0.001	7.394	0.089	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.683	3.190	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.824	0.000	0.028	0.000	1	5	NaN	NaN	0.657	0.000	4.301	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.313	0.017	0.000	0.001	1	5	0.981	0.958	0.094	0.001	13.967	0.261	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.000	0.000	9.345	5.708	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.877	0.000	0.028	0.000	1	100	NaN	NaN	0.653	0.000	4.403	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.371	0.024	0.000	0.004	1	100	0.977	0.972	0.525	0.006	8.324	0.110	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.005	0.000	0.000	0.005	1	100	1.000	1.000	0.001	0.000	6.658	2.103	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.711	0.000	0.022	0.000	-1	5	NaN	NaN	0.435	0.000	1.635	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.002	0.001	0.000	-1	5	0.982	0.979	0.006	0.001	3.825	0.438	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	19.659	15.969	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.700	0.000	0.023	0.000	-1	100	NaN	NaN	0.429	0.000	1.633	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.038	0.000	0.000	0.000	-1	100	0.983	0.982	0.001	0.000	35.136	9.193	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	21.891	18.451	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.700	0.000	0.023	0.000	-1	1	NaN	NaN	0.429	0.000	1.633	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.020	0.000	0.001	0.000	-1	1	0.977	0.982	0.001	0.000	19.745	6.635	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	21.102	17.322	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.709	0.000	0.023	0.000	1	1	NaN	NaN	0.430	0.000	1.650	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.019	0.000	0.001	0.000	1	1	0.977	0.978	0.001	0.000	25.765	9.661	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.273	4.452	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.692	0.000	0.023	0.000	1	5	NaN	NaN	0.424	0.000	1.632	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.021	0.000	0.001	0.000	1	5	0.982	0.978	0.001	0.000	29.761	11.012	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.832	5.015	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.696	0.000	0.023	0.000	1	100	NaN	NaN	0.421	0.000	1.652	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.046	0.000	0.000	0.000	1	100	0.983	0.979	0.006	0.001	7.699	0.878	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.630	4.635	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.547	0.0	0.877	0.000	random	NaN	30	NaN	0.372	0.0	1.470	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.419	0.000	random	0.001	30	0.001	0.000	0.0	7.994	4.803	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.540	8.087	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.514	0.0	0.935	0.000	k-means++	NaN	30	NaN	0.404	0.0	1.272	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.413	0.000	k-means++	0.001	30	0.001	0.000	0.0	7.575	5.208	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.086	7.359	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.035	0.0	3.977	0.000	random	NaN	30	NaN	2.585	0.0	2.335	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	14.720	0.000	random	0.002	30	0.001	0.000	0.0	6.431	3.311	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.021	0.001	random	1.000	30	1.000	0.000	0.0	8.431	5.079	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	5.901	0.0	4.067	0.000	k-means++	NaN	30	NaN	2.749	0.0	2.147	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.001	0.0	16.660	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.435	2.402	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.021	0.001	k-means++	1.000	30	1.000	0.000	0.0	8.474	5.047	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.214	0.0	0.015	0.000	k-means++	NaN	20	NaN	0.032	0.0	6.766	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.202	0.000	k-means++	0.004	20	-0.001	0.001	0.0	2.573	0.485	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	9.460	6.460	See	See
3	KMeans_short	fit	10000	10000	2	20	0.079	0.0	0.041	0.000	random	NaN	20	NaN	0.087	0.0	0.907	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.202	0.000	random	0.001	20	-0.001	0.001	0.0	2.438	0.424	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.419	6.689	See	See
6	KMeans_short	fit	10000	10000	100	20	0.578	0.0	0.277	0.000	k-means++	NaN	20	NaN	0.141	0.0	4.087	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	7.104	0.000	k-means++	0.285	20	0.302	0.001	0.0	2.055	0.339	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	7.897	4.516	See	See
9	KMeans_short	fit	10000	10000	100	20	0.195	0.0	0.822	0.000	random	NaN	20	NaN	0.340	0.0	0.572	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.828	0.000	random	0.340	20	0.304	0.001	0.0	2.174	0.352	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	7.818	4.140	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.159	0.0	[-0.10572992]	0.000	NaN	NaN	NaN	NaN	NaN	1.766	0.0	6.319	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[54.31415648]	0.000	NaN	NaN	NaN	NaN	0.543	0.000	0.0	0.918	0.428	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.22834113]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.0	0.460	0.401	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.721	0.0	[2.88587009]	0.001	NaN	NaN	NaN	NaN	NaN	0.729	0.0	0.988	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[132.75694692]	0.000	NaN	NaN	NaN	NaN	0.260	0.003	0.0	0.587	0.106	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[20.53491476]	0.000	NaN	NaN	NaN	NaN	1.000	0.001	0.0	0.159	0.104	See	See

Ridge: scikit-learn (1.0.dev0) vs. daal4py (2021.2.3)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.174	0.0	0.461	0.0	NaN	NaN	NaN	0.174	0.0	0.995	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	8.124	0.0	NaN	NaN	0.091	0.016	0.0	0.603	0.008	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.130	0.0	NaN	NaN	NaN	0.000	0.0	0.747	0.755	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.384	0.0	0.578	0.0	NaN	NaN	NaN	0.221	0.0	6.260	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	5.158	0.0	NaN	NaN	1.000	0.000	0.0	0.694	0.426	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.014	0.0	NaN	NaN	NaN	0.000	0.0	0.666	0.701	See	See

Gradient boosting: randomized HPO benchmarks

reporting_hpo = ReportingHpo(config="config.yml")
reporting_hpo.run()

Raw fit times vs. accuracy scores

Raw predict times vs. accuracy scores

Smoothed HPO Curves

The shaded areas represent boostrapped quartiles.

Speedup barplots

Speedup curves